The presence of skin color is useful as a cue for detecting people in real-world photographic images. Skin color detection plays an important role in applications such as people tracking, blocking mature-content web images, and facilitating human-computer interaction. Skin color detection may also serve as an enabling technology for face detection, localization, recognition, and/or tracking; video surveillance; and image database management. These and other applications are becoming more significant with the adoption of portable communications devices, such as cellular telephones, that are equipped with digital video or still photo cameras. For example, the ability to localize faces may be applied to a more efficient use of bandwidth by coding a face region of an image with better quality and using a higher degree of compression on the image background.
The reflectance of a skin surface is usually determined by its thin surface layer, or “epidermis,” and an underlying thicker layer, or “dermis.” Light absorption by the dermis is mainly due to ingredients in the blood such as hemoglobin, bilirubin, and beta-carotene, which are basically the same for all skin types. However, skin color is mainly determined by the epidermis transmittance, which depends on the dopa-melanin concentration and hence varies among human races.
Skin color appearance can be represented by using this reflectance model and incorporating camera and light source parameters. The main challenge is to make skin detection robust to the large variations in appearance that can occur. Skin appearance changes in color and shape, and it is often affected by occluding objects such as clothing, hair, and eyeglasses. Moreover, changes in intensity, color, and location of light sources can affect skin appearance, and other objects within the scene may complicate the detection process by casting shadows or reflecting additional light. Many other common objects are easily confused with skin, such as copper, sand, and certain types of wood and clothing. An image may also include noise appearing as speckles of skin-like color.
One conventional approach to skin detection begins with a database of hundreds or thousands of images with skin area (such as face and/or hands). This database serves as a training surface set from which statistics distinguishing skin regions from non-skin regions may be derived. The color space is segmented according to these statistics, and classifications are made based on the segmentation. One disadvantage is that the database images typically originate from different cameras and are taken under different illuminations.